SOTAVerified

Saliency Prediction

A saliency map is a model that predicts eye fixations on a visual scene. Saliency prediction is informed by the human visual attention mechanism and predicts the possibility of the human eyes to stay in a certain position in the scene.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 268 papers

TitleStatusHype
BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object DetectionCode1
Learning to Predict Salient Faces: A Novel Visual-Audio Saliency ModelCode1
A Gated Fusion Network for Dynamic Saliency Prediction—0
Audiovisual Saliency Prediction in Uncategorized Video Sequences based on Audio-Video Correlation—0
Light Field Saliency Detection With Dual Local Graph Learning and Reciprocative Guidance—0
Rethinking 360deg Image Visual Attention Modelling With Unsupervised Learning.Code0
Statistically Consistent Saliency Estimation—0
Learning Category-level Shape Saliency via Deep Implicit Surface Networks—0
ViNet: Pushing the limits of Visual Modality for Audio-Visual Saliency PredictionCode1
ATSal: An Attention Based Architecture for Saliency Prediction in 360 VideosCode1
SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater RobotsCode1
A Psychophysical Oriented Saliency Map Prediction Model—0
RSKDD-Net: Random Sample-based Keypoint Detector and DescriptorCode1
Hierarchical Domain-Adapted Feature Learning for Video Saliency PredictionCode1
Uncertainty Inspired RGB-D Saliency DetectionCode1
RecSal : Deep Recursive Supervision for Visual Saliency Prediction—0
A Compact Deep Architecture for Real-time Saliency Prediction—0
FastSal: a Computationally Efficient Network for Visual Saliency PredictionCode1
n-Reference Transfer Learning for Saliency PredictionCode1
Saliency Prediction with External Knowledge—0
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains—0
n-Reference Transfer Learning for Saliency PredictionCode1
ITSELF: Iterative Saliency Estimation fLexible Framework—0
"Looking at the Right Stuff" - Guided Semantic-Gaze for Autonomous Driving—0
Select, Supplement and Focus for RGB-D Saliency DetectionCode1
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All datasetsSALECISALICONMIT300CAT2000

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TransalnetKL0.87—Unverified
2TempSALKL0.71—Unverified
3SSwin transformerKL0.65—Unverified
4ECT-SALKL0.58—Unverified
5SUMKL0.47—Unverified
#ModelMetricClaimedVerifiedStatus
1SUMAUC0.88—Unverified
2SalNAS-XL + Self-KDAUC0.87—Unverified
3TempSALAUC0.87—Unverified
4MDS-ViTNetAUC0.87—Unverified
5TranSalNetAUC0.87—Unverified
#ModelMetricClaimedVerifiedStatus
1SUMAUC-Judd0.91—Unverified
2TranSalNetAUC-Judd0.87—Unverified
#ModelMetricClaimedVerifiedStatus
1SUMKL0.27—Unverified